Classified products.
Matched across sellers.
Every catalog.
Turn raw ecommerce data into structured product intelligence. Taxonomy, attributes, signals, English. The same product matched across every seller.
Two jobs sit between scraped ecommerce data and a catalog you can actually query. Classification: putting every product on a category path with structured attributes and signals. Matching: collapsing the same physical product across every seller into one record. Extralt does both, on any ecommerce site. Built for catalog teams, market intelligence analysts, and anyone building products on top of ecommerce data.
product data enrichment · three angles
One catalog, three angles on its structure.
cross-source matching
One product, every seller, one record.
Same physical product, fifteen seller listings, one canonical record. Where GTIN or brand+MPN matches, the records resolve by identifier. Where they do not, embedding similarity inside the same brand and category does the work. You end up with apples-to-apples comparisons across the open web without writing per-merchant mapping rules.
Variants stay variant-level. A shoe in 4 colors and 12 sizes resolves to 4 records, one per color, with sizes nested inside each. Listings point to variants, variants point to one canonical product. Cross-seller price comparison and assortment analysis fall out of that shape, no schema rebuild on your side.
- Cross-seller resolutionSame product across 15 sellers resolves to one canonical record. Listings keep their per-seller details. The product identity stays consistent.
- Variant-level granularityColor and material become separate records. Size and other size-like options stay nested. A 4 color × 12 size shoe is 4 records, not 48.
- Identifier unificationGTIN, MPN, and per-seller IDs from every listing union onto the canonical record. One lookup gets you every identifier the market uses.
- Multi-language normalizationA French and a German listing of the same product end up with comparable English fields. The original-language text stays on the record for display.
deliverables
What you get
Output
Variants + listings
One record per option combo per country
Coverage
Open web
Any ecommerce site, no opt-in
Languages
Original + English
Source text kept alongside translation
Access
API · SQL · CSV
Bring your own tool
why extralt
Built for catalogs that come from everywhere.
One schema, every source
A marketplace and a regional retailer come out with the same fields a DTC site does. No per-source parsing logic in your downstream pipeline.
Ground truth, not feeds
We classify what is on the page, not what a merchant chose to submit in a feed. Feed quality and merchant cooperation stop being your problem.
No taxonomy work on you
Categories, attributes, and signals come pre-mapped. You inherit a working schema on day one, instead of building one over six months.
All of this runs on Enrich, the second stage of the Extralt pipeline. Captures from Extract become structured variants and listings, with taxonomy, attributes, signals, and English normalization applied at the same time.
who it's for
For teams whose data comes from many sources and needs to look like one.
- Catalog & merchandising teamsNormalize supplier feeds, marketplace listings, and scraped competitor data into one shape. Stop maintaining mapping rules per source.
- Market intelligence analystsSlice categories by brand, attributes, and price tier across the whole market. See assortment shape without per-site catalog parsing.
- Product & data teams building on topSearch, recommendations, and agent-facing APIs that need product structure they can reason about. Inherit the schema instead of building it.
faq
Common questions
Start enriching product data today.
Pay-as-you-go credits. No contract. See your first enriched variants in minutes.